Potential of Standardization and Certification for Successful Lean Implementations
Bibliographic record
Abstract
A successful lean implementation is the key for lean enterprise transformation. However, many companies are struggling to change the culture in their system and are having problems in adapting lean principles. In this article, we discuss whether lean standardization and professional lean certification (LS&C) has the potential to promote successful lean implementation leading to a lean enterprise transformation. For this purpose, we first analyzed the concepts of LS&C and reviewed the existing ones, including J4000 through literature review and personal communications with lean experts. We also conducted a survey among lean professionals to get feedback about their attitudes toward LS&C. The survey results suggest that there is significant support for LS&C, as around 60% of the survey attendants believe LS&C would eliminate problems in implementing lean principles. However, the awareness of existing standards among lean practitioners is very low, which indicates the need for development of new lean standards and/or better promotion. Our survey results also suggest that the level of support for lean standardization depends on many factors, including positions of the professionals and extent of companies’ lean experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".